1. Technical Field
Exemplary embodiments of the present invention relate to scalable online hierarchical meta-learning, and more particularly, to a system and method for implementing scalable online hierarchical meta-learning.
2. Discussion of Related Art
With the emergence of sensor technologies and the general instrumentation of the real world, big data analytics are being utilized more frequently to transform large datasets collected by sensors into actionable intelligence. In addition to having a large volume of information, large datasets may also be defined by their heterogeneity and distributed nature. Learners may be utilized to extract and analyze data from a large dataset. Existing distributed data mining techniques may be characterized by limited data access as the result of the application of local learners having limited access to a large and distributed dataset. The applications of such distributed data mining systems to real-world problems across different sites and by different institutions offer the promise of expanding current frontiers in knowledge acquisition and data-driven discovery within the bounds of data privacy constraints that prevent the centralization of all the data for mining purposes.